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<meta name="description" content="完成了EDA，下面本来该进行特征工程了。但是有个问题是，这个题目是金融相关的，为了防止使用未来函数，要求用题目给函数来提交，大概就是把测试数据一个一个的提供，而不是一起提供吧。这就带来一个问题就是连填充空值也有问题，比如用均值填充，在训练数据上可以，在测试数据上就失败了。在本地能跑通的程序，在kaggle上却不行。也有办法，比如记录训练集的每列的均值，拿来填充测试集的均值。以后再试吧。先简单地用0">
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          <h1 class="post-title" itemprop="name headline">量化投资学习笔记95——kaggle量化投资比赛记录4-尝试各种模型</h1>
        

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        <p>完成了EDA，下面本来该进行特征工程了。但是有个问题是，这个题目是金融相关的，为了防止使用未来函数，要求用题目给函数来提交，大概就是把测试数据一个一个的提供，而不是一起提供吧。这就带来一个问题就是连填充空值也有问题，比如用均值填充，在训练数据上可以，在测试数据上就失败了。在本地能跑通的程序，在kaggle上却不行。也有办法，比如记录训练集的每列的均值，拿来填充测试集的均值。以后再试吧。先简单地用0来填充空值。<br>下面尝试一下各种预测算法吧。线性回归试了一下，评分不高，就主要用分类模型和聚类吧。<br>sklearn文档里有个算法选择图:<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/01.png"><br>就用这些。<br>先写评估模型的函数</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 对模型进行交叉验证</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">cross_val</span>(<span class="params">model, X, Y, cv = <span class="number">5</span></span>):</span></span><br><span class="line">    scores = cross_val_score(model, X, Y, cv=cv)</span><br><span class="line">    score = scores.mean()</span><br><span class="line">    <span class="keyword">return</span> score</span><br></pre></td></tr></table></figure>
<p>用的方法是k折交叉验证，即将所有数据分成k份，不重复地每次取一份做测试集，其它几份作为训练集，计算该模型在数据集上的MSE，最后取平均数。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 模型评估</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">evalution</span>(<span class="params">model, test</span>):</span></span><br><span class="line">    X = test.loc[:, test.columns.<span class="built_in">str</span>.contains(<span class="string">&quot;feature&quot;</span>)].values</span><br><span class="line">    y_true = test.action.values</span><br><span class="line">    y_pred = model.predict(X)</span><br><span class="line">    target_names = [<span class="string">&quot;1&quot;</span>, <span class="string">&quot;0&quot;</span>]</span><br><span class="line">    result = classification_report(y_true, y_pred, target_names = target_names)</span><br><span class="line">    print(result, <span class="built_in">type</span>(result))</span><br></pre></td></tr></table></figure>
<p>用这些评分函数回测一下逻辑回归模型:<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/02.png"><br>具体的含义:<br>precision是预测精确度，被预测的结果的准确性，分母是预测出的数据。TP/(TP+FP)，比瞎猜好不了多少啊。<br>recall是召回率，即所有真实的样本有多少被正确预测出来了。分母是原数据。TP/(TP+FN)。<br>f1-score:二者的调和均值。等于1时最佳，等于0时最差。2<em>precision</em>recall/(precision+recall)<br>在二分类场景中，正标签的召回率称为敏感度（sensitivity），负标签的召回率称为特异性（specificity）。<br>比瞎猜好那么一点点。<br>另外再画一下ROC曲线。<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/03.png"><br>图像越偏左上角越好，可以看到ROC几乎跟对角线重合，也就是跟瞎猜差不多了。<br>从<a target="_blank" rel="noopener" href="https://blog.csdn.net/quiet_girl/article/details/70830796%E5%80%9F%E5%BC%A0%E5%9B%BE%EF%BC%9A">https://blog.csdn.net/quiet_girl/article/details/70830796借张图：</a><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/04.png"><br>曲线与DBC围成的面积越大越好。<br>再画一下学习曲线：<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/05.png"><br>提交到kaggle的结果<br>Public Score:2038.005<br>下面就用其它分类算法来尝试。开始用20%的数据，结果在服务器上跑了一夜，12小时，还没有出结果。后来就改用1%的数据，跑了好几个小时才完。<br>导入相关的库</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 支持向量机</span></span><br><span class="line"><span class="keyword">from</span> sklearn.svm <span class="keyword">import</span> SVC, LinearSVC</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/06.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/07.png"></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 随机森林</span></span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/08.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/09.png"></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># KNN算法</span></span><br><span class="line"><span class="keyword">from</span> sklearn.neighbors <span class="keyword">import</span> KNeighborsClassifier</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/10.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/11.png"></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 朴素贝叶斯算法</span></span><br><span class="line"><span class="keyword">from</span> sklearn.naive_bayes <span class="keyword">import</span> GaussianNB</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/12.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/13.png"></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># SGD算法</span></span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> SGDClassifier</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/14.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/15.png"></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 决策树算法</span></span><br><span class="line"><span class="keyword">from</span> sklearn.tree <span class="keyword">import</span> DecisionTreeClassifier</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/16.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/17.png"><br>从ROC曲线来看，这几个模型都不太好。再来看学习曲线，怎么看呢？<br>再从<a target="_blank" rel="noopener" href="https://ljalphabeta.gitbooks.io/python-/content/debugging.html">https://ljalphabeta.gitbooks.io/python-/content/debugging.html</a> 借张图吧：<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0178-QTLearn/68/18.png"><br>左上角的是高偏差的模型，训练集和测试集准确率都很低(低于我们可以接受的水平，比如这里，准确率仅稍微高于0.5，比瞎猜只好那么一点），很可能是欠拟合。解决方法是增加模型参数，减小正则项。上面的模型中，朴素贝叶斯和SGD算法属于此类。<br>右上角是高方差的模型，表现是训练集和测试集准确率相差太多。可能属于模型过拟合了。解决方法是增加训练集或降低模型复杂度，如增大正则项，或通过特征选择减少特征数。上面的随机森林，支持向量机，knn，决策树等模型属于这种情况。<br>右下角是我们想要的，随着样本数量的上升，训练集和测试集的准确率收敛到一个水平，而这个水平是我们可以接受的水平。<br>挨个提交看看吧。kaggle提交太慢了。结果先不发了，应该也不好。下次试试模型集成。</p>
<p>本文代码：<br><a target="_blank" rel="noopener" href="https://github.com/zwdnet/JSMPwork/blob/main/test_work.py">https://github.com/zwdnet/JSMPwork/blob/main/test_work.py</a><br><a target="_blank" rel="noopener" href="https://github.com/zwdnet/JSMPwork/blob/main/tools.py">https://github.com/zwdnet/JSMPwork/blob/main/tools.py</a></p>
<p>我发文章的三个地方，欢迎大家在朋友圈等地方分享，欢迎点“在看”。<br>我的个人博客地址：<a href="https://zwdnet.github.io/">https://zwdnet.github.io</a><br>我的知乎文章地址： <a target="_blank" rel="noopener" href="https://www.zhihu.com/people/zhao-you-min/posts">https://www.zhihu.com/people/zhao-you-min/posts</a><br>我的微信个人订阅号：赵瑜敏的口腔医学学习园地</p>
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